DUFOMap: Efficient Dynamic Awareness Mapping

Daniel Duberg^1,*    Qingwen Zhang^1,*    Mingkai Jia²    Patric Jensfelt^1   
*Co-first author    ¹KTH    ²HKUST   

[video coming soon] [poster coming soon]. Accepted by RA-L'24.

Quick Demo: Run with the same parameter setting without tuning for different sensor (e.g 16, 32, 64, and 128 channel LiDAR and Livox-series mid360), the following shows the data collected from:

Leica-RTC360	128-channel LiDAR	Livox-mid360

0. Setup

Clone and init submodule quickly:

git clone --recursive -b main --single-branch https://github.com/Kin-Zhang/dufomap.git

# 在内地的同学可以尝试下面gitee加速：
git clone --recursive -b main --single-branch https://gitee.com/kin-zhang/dufomap

Choose setup on your own environment or inside docker.

Environment

Since Ranges (std::range) and #include <concepts> first existed in C++20 and GCC 10

sudo apt update && sudo apt install gcc-10 g++-10
sudo apt install libtbb-dev liblz4-dev liblzf-dev

Docker

Dockerfile is provided, you can build or directly pull by:

# option 1: build
docker build -f Dockerfile -t zhangkin/dufomap .

# option 2: pull
docker pull zhangkin/dufomap

Then you can run a container with the following command:

docker run -it --rm --name dufomap -v /home/kin/data:/home/kin/data zhangkin/dufomap /bin/zsh
# you can also login as root to install pkg in existing container you want through:
docker exec -it -u 0 dufomap /bin/zsh

1. Build & Run

Build:

cmake -B build -D CMAKE_CXX_COMPILER=g++-10 && cmake --build build

Prepare Data: Teaser data (KITTI 00: 384.4Mb) can be downloaded via follow commands, more data detail can be found in the dataset section or format your own dataset follow custom dataset section.

wget https://zenodo.org/records/8160051/files/00.zip
unzip 00.zip -d data

Run:

./build/dufomap_run data/00 assets/config.toml

2. Evaluation

Please reference to DynamicMap_Benchmark for the evaluation of DUFOMap and comparison with other dynamic removal methods.

Evaluation Section link

Acknowledgements

Thanks to HKUST Ramlab's members: Bowen Yang, Lu Gan, Mingkai Tang, and Yingbing Chen, who help collect additional datasets.

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation including the WASP NEST PerCorSo.

Feel free to explore other projects that use ufomap (attach code links as follows):

• RA-L'24 DUFOMap, Dynamic Awareness
• RA-L'23 SLICT, SLAM
• RA-L'20 UFOMap, Mapping Framework

Citation

Please cite our works if you find these useful for your research.

@article{daniel2024dufomap,
  author={Duberg, Daniel and Zhang, Qingwen and Jia, Mingkai and Jensfelt, Patric},
  journal={IEEE Robotics and Automation Letters}, 
  title={{DUFOMap}: Efficient Dynamic Awareness Mapping}, 
  year={2024},
  volume={9},
  number={6},
  pages={5038-5045},
  doi={10.1109/LRA.2024.3387658}
}
@article{duberg2020ufomap,
  author={Duberg, Daniel and Jensfelt, Patric},
  journal={IEEE Robotics and Automation Letters}, 
  title={{UFOMap}: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown}, 
  year={2020},
  volume={5},
  number={4},
  pages={6411-6418},
  doi={10.1109/LRA.2020.3013861}
}
@inproceedings{zhang2023benchmark,
  author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric},
  booktitle={IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, 
  year={2023},
  pages={608-614},
  doi={10.1109/ITSC57777.2023.10422094}
}